Sustainability of a Primary Care–Driven eConsult Service
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Excessive wait times for specialist appointments pose a serious barrier to patient care. To improve access to specialist care and reduce wait times, we launched the Champlain BASE (Building Access to Specialists through eConsultation) eConsult service in April 2011. The objective of this study is to report on the impact of our multiple specialty eConsult service during the first 5 years of use after implementation, with a focus on growth and sustainability. METHODS: We conducted a cross-sectional study of all eConsult cases submitted between April 1, 2011 and April 30, 2016, and measured impact with system utilization data and mandatory close-out surveys completed at the end of each eConsult. Impact indicators included time interval to obtain specialist advice, effect of specialist advice on the primary care clinician's course of action, and rate of avoidance of face-to-face visits. RESULTS: A total of 14,105 eConsult cases were directed to 56 different medical specialty groups, completed with a median response time of 21 hours, and 65% of all eConsults were resolved without a specialist visit. We observed rapid growth in the use of eConsult during the study period: 5 years after implementation the system was in use by 1,020 primary care clinicians, with more than 700 consultations taking place per month. CONCLUSIONS: This study presents the first in-depth look at the growth and sustainability of the multispecialty eConsult service. The results show the positive impact of an eConsult service and can inform other regions interested in implementing similar systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it